CFD for Data Centers: Driving Down Cost and Improving Ease of Use

The growing adoption of smartphones, PCs and tablets is increasing demands on data center processing. For every handheld device attached to the cloud, a data center somewhere is processing the information. The resulting growth in data center demand is expected to continue to rise at the rate of 14% per year, according Tier 1 Research.* To compete in this market, data center designers and operators are squeezing the most out of the energy envelope. This is especially important to colocation providers who compete on the basis of operational overhead with other providers in their market. If they can deliver IT processing capacity at lower cost, they gain a distinct competitive advantage. Over the past several years, computational fluid dynamics (CFD) modeling has proven to be a reliable method for optimizing the energy efficiency of the data center as well as accurately predicting the failure-mode conditions associated with cooling system failure.

For the past 30 years, CFD has been heavily used in industries such as aerospace and F1 racing, as the systems are complex, exotic and expensive. But things are beginning to change. Using advanced graphical user interfaces (GUIs) combined with a “SaaS” (software as a service) methods of application delivery, data center designers and facility managers are able to use the same technology to design data centers that the Ferrari F1 team uses to design its race cars, and at a much lower cost of entry. This approach greatly broadens access to this technology, which has been well proven in optimizing energy efficiency, reducing hot spots and accurately predicting cooling failure. Using a CFD application to model the design of data centers has been a “best practice” for a number of years, but its broad-based use as a tool to help manage the operation of a data center has been precluded by its complexity and high price.

Benefits of CFD Monitoring

PUE Prediction

The energy to power a data center is composed of the power to drive the servers plus the power to cool the servers and any other ancillary devices such as PDUs, pumps and lighting. A common method for measuring the energy efficiency of a data center is PUE (power utilization effectiveness). The PUE of a data center is expressed in the relationship shown in Figure 1. A “good” data center is represented by a PUE of 1.2 or lower, suggesting that an additional 20% of the total power to operate the data center is powering non-IT equipment.

Figure 1

When reviewing the items that constitute the non-IT components of the calculations, the dominant parameters are related to the power required to drive the cooling systems. In fact, studies have shown that 75% of the non-IT power is consumed by the cooling system. This is why the recent incorporation of free cooling has become popular to reduce overall cooling power consumption. Free cooling uses outside ambient temperatures to cool the data center whenever the outside temperature reaches a sufficiently low level. The role of CFD modeling in any of these designs is to insure that the cool air coming from the ducts, tiles or CRAH outlet reaches all the servers uniformly. This process of modeling the airflow and the resulting convective heat transfer is essential to the overall design and operation of a data center, particularly as it relates to failure-mode studies.

Cooling Failure Prediction

Failure model prediction is a key benefit that CFD modeling can provide. All cooling units need to be serviced periodically, and they completely fail occasionally. Predicting the thermal condition of the room ahead of the failure is vitally important to the data center operator. Knowing which servers will need to be shut down, or which cooling units are the most critical to the room, can be accurately and precisely predicted well in advance, and procedures can then be developed to follow in those cases.

What to Look for in a CFD Model

Here are some key factors to consider when seeking a CFD model:

User friendly: The last thing you need is another complex system—be sure to choose a model that is easy to use, is easy to understand and can be easy displayed to others (such as the C-Suite) who may not be as tech savvy.

Scalability: Be sure to choose a solution that will scale with you as new components (files, CRACs, racks, tiles, servers and so on) are added.

Flexible and Extensible: Be sure the system is flexible and extensible for integration with either upstream or downstream applications. For example, data from other DCIM applications can be fed directly into the system to speed model preparation.

Maximum Output: Look for a system that has options for output reports to ensure variety in how the information can be presented (graphs, charts, etc.). Every organization has its own methods, and this feature will benefit the usefulness of the data—it is meaningless unless it can convey the message to decision makers.

As the price and complexity of CFD modeling continues to decline, the use of this technology will become more widespread. Data center design engineers will use the tool to create more-energy-efficient and robust designs. The CFD model will then be passed along to the data center operators for ongoing use as a planning and prediction tool. As changes are made to the servers, cooling units or rack locations, new information would automatically be passed to the CFD model from upstream DCIM software, or from sensors in the room. The CFD model would run in the background and predict any potential problems that may occur, and it would be preset with failure scenarios (such as CRAC failure scenarios) that alert the operator to a potential situation well in advance. This type of “predictive modeling” has real value for data center operators. By combining an SaaS-based delivery method, the low cost of cloud computing and an easy-to-use GUI, the benefits of CFD modeling can enjoyed by a much wider audience and can become an integral part of the entire data center design and operational process.

About the Author

Paul Bemis has over 20 years experience in the high technology marke, having held executive positions at ANSYS, Fluent, HP and Apollo Computer. He is currently the President and CEO of Applied Math Modeling, a supplier of the CoolSim data center modeling software and services. Bemis has a BSME from the University of New Hampshire and an MSEE and MBA from Northeastern University in Boston.